Experimental modelling and genetic algorithm-based optimisation of friction stir welding process parameters for joining of dissimilar AA5083-O and AA6063-T6 aluminium alloys
IR@NML: CSIR-National Metallurgical Laboratory, Jamshedpur
View Archive Info| Field | Value | |
| Title |
Experimental modelling and genetic algorithm-based optimisation of friction stir welding process parameters for joining of dissimilar AA5083-O and AA6063-T6 aluminium alloys
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| Creator |
Gupta, S K
Pandey, K N Kumar, R |
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| Subject |
Aluminium Alloys
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| Description |
Friction stir welding (FSW) is a solid state joining process and one of the most promising technique for defect free joining of aluminium alloys. In this paper, second order regression modelling and genetic algorithm-based optimisation of FSW process parameters is presented for joining of dissimilar AA5083-O and AA6063-T6 aluminium alloys. For developing the regression model, experiments were performed as per L27 orthogonal array and models were developed with the help of MINITAB software. For genetic algorithm-based process parameter optimisation, regression models were considered as objective functions. The regression models have been found satisfactory for predicting the responses at 99% confidence level. The derived set of optimal process parameters were found as tool rotational speed of 900 rpm, welding speed of 60 mm/min, shoulder diameter of 18 mm and pin diameter of 5 mm for maximum tensile strength and minimum grain size.
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| Publisher |
Inderscience Enterprises Ltd
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| Date |
2018
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| Type |
Article
PeerReviewed |
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| Relation |
https://doi.org/10.1504/IJMPT.2018.10010366
http://eprints.nmlindia.org/7907/ |
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| Identifier |
Gupta, S K and Pandey, K N and Kumar, R (2018) Experimental modelling and genetic algorithm-based optimisation of friction stir welding process parameters for joining of dissimilar AA5083-O and AA6063-T6 aluminium alloys. International Journal of Materials & Product Technology, 56(3) (IF-0.802). pp. 253-270.
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